Smooth pursuit eye movements provide meaningful insights and information on
subject's behavior and health and may, in particular situations, disturb the
performance of typical fixation/saccade classification algorithms. Thus, an
automatic and efficient algorithm to identify these eye movements is paramount
for eye-tracking research involving dynamic stimuli. In this paper, we propose
the Bayesian Decision Theory Identification (I-BDT) algorithm, a novel
algorithm for ternary classification of eye movements that is able to reliably
separate fixations, saccades, and smooth pursuits in an online fashion, even
for low-resolution eye trackers. The proposed algorithm is evaluated on four
datasets with distinct mixtures of eye movements, including fixations,
saccades, as well as straight and circular smooth pursuits; data was collected
with a sample rate of 30 Hz from six subjects, totaling 24 evaluation datasets.
The algorithm exhibits high and consistent performance across all datasets and
movements relative to a manual annotation by a domain expert (recall: \mu =
91.42%, \sigma = 9.52%; precision: \mu = 95.60%, \sigma = 5.29%; specificity
\mu = 95.41%, \sigma = 7.02%) and displays a significant improvement when
compared to I-VDT, an state-of-the-art algorithm (recall: \mu = 87.67%, \sigma
= 14.73%; precision: \mu = 89.57%, \sigma = 8.05%; specificity \mu = 92.10%,
\sigma = 11.21%). For algorithm implementation and annotated datasets, please
contact the first author.

本文提出了基于贝叶斯决策理论的眼动分类算法 I-BDT，该算法可自动、高效地对动态视觉刺激条件下的注视、扫视和平滑追踪进行三元分类，对于低分辨率眼动追踪器具有较好的鲁棒性和一致性，评估结果显示该算法在本文给出的四个数据集上表现优越，其实现和注释数据集请联系第一作者。